DBMS > IRONdb vs. KairosDB vs. NSDb vs. RocksDB vs. VoltDB

System Properties Comparison IRONdb vs. KairosDB vs. NSDb vs. RocksDB vs. VoltDB

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Distributed Time Series DBMS based on Cassandra or H2

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Time Series DBMS

Key-value store

Relational DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.62
Rank	#239	Overall
	#20	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

www.circonus.com/solutions/time-series-database/

github.com/kairosdb/kairosdb

nsdb.io

rocksdb.org

www.voltdb.com

Technical documentation

docs.circonus.com/irondb/category/getting-started

kairosdb.github.io

nsdb.io/Architecture

github.com/facebook/rocksdb/wiki

docs.voltdb.com

Developer

Circonus LLC.

Facebook, Inc.

VoltDB Inc.

Initial release

2017

2013

2017

2013

2010

Current release

V0.10.20, January 2018

1.2.2, November 2018

8.11.4, April 2024

11.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

BSD

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C and C++

Java

Java, Scala

C++

Java, C++

Server operating systems

Linux

Linux
OS X
Windows

Linux
macOS

Linux

Linux
OS X

for development

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

yes: int, bigint, decimal, string

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

SQL-like query language

yes

only a subset of SQL 99

APIs and other access methods

HTTP API

Graphite protocol
HTTP REST
Telnet API

gRPC
HTTP REST
WebSocket

C++ API
Java API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

Java
JavaScript

Node.js
PHP
Python

Java
Scala

C
C++
Go
Java
Perl
Python
Ruby

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes, in Lua

Java

Triggers

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

Sharding

based on Cassandra

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

selectable replication factor

based on Cassandra

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency per node, eventual consistency across nodes

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

yes

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Using Apache Lucene

yes

Snapshots and command logging

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

simple password-based access control

Users and roles with access to stored procedures

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